Why We Need to Slow Down When AI Speeds Us Up

AI coding assistants are here, and they’re changing the way we build software fast.

Tools like GitHub Copilot, ChatGPT, and Replit’s Ghostwriter can generate boilerplate code in seconds, refactor complex logic on command, and even help debug entire applications. It’s impressive, empowering and, if we’re not careful, dangerous.

Because when the pace picks up, experience matters more than ever.

Speed Creates Blind Spots

AI doesn’t “understand” context the way a seasoned engineer does. It doesn’t know your edge cases, business logic, or the last three outages you fought in production. It’s pulling from the collective memory of the internet, not from your team’s tribal knowledge.

That’s why I believe a solid pre-production checklist isn’t optional anymore it’s essential.

When things move quickly, we need intentional pauses built into our process. QA can’t be an afterthought. If we want to maintain quality and reliability, we need to slow down on purpose.

A Checklist to Keep Us Honest

Here’s a checklist I use to help catch mistakes before they hit production, especially when working with AI-assisted code:

1. Code Quality & Understanding

• Was the AI-generated code reviewed by a human?

• Does the author understand what the AI produced and why?

• Does it follow our naming conventions and project standards?

2. Testing

• Are all relevant unit and integration tests in place and passing?

• Are we covering critical paths and edge cases?

• Has someone done a manual sanity check?

3. Security

• Are there any hardcoded secrets or credentials?

• Has the AI introduced outdated or unsafe patterns?

• Have dependencies been scanned for vulnerabilities?

4. Versions Matter

• Are all dependencies pinned (e.g. requirements.txt, package-lock.json)?

• Has the AI pulled in newer libraries or made implicit upgrades?

• Are runtime environments (e.g., Python, Node.js, Terraform, Docker) consistent with staging and prod?

5. Documentation

• Are code comments in place where needed?

• Has the README or relevant docs been updated?

• Do we know where the AI logic came from — and can we explain it?

6. Deployment Readiness

• Does the build pipeline pass?

• Is there a rollback plan?

• Have we tested in a non-prod environment?

7. Final Pause

• Has a second set of eyes looked at the code?

• Have we accounted for edge cases and performance?

• Are we about to ship with confidence?

Build In the Breaks

AI will keep getting faster, better, and more convincing. But it’s still our job to think. That means introducing deliberate breaks into our workflows checkpoints where we can stop, breathe, and review.

In a world where code can be written in seconds, QA is our last line of defence.

Let’s not just be fast. Let’s be thoughtful. Let’s be great at QA.